As a PC enthusiast, I love pitting hardware solutions against each other to determine their relative performance when completing a particular task. This process is also known as “Benchmarking.” Benchmarking results are usually considered the best tool to evaluate the merits of competing systems when making a purchase decision.

In this 3-part blog series, we’ll discuss how to build a system, with an emphasis on benchmarking GPU performance for Deep Learning using Ubuntu 18.04, NVIDIA GPU Cloud (NGC) and TensorFlow.

According to Data Science Central, a leading online resource for data practitioners, forecasts predict the big data market will approach $203 billion by 2020. Data science is powering the engine of modern enterprise – every industry from retail to financial services to healthcare is deriving insight from data to improve competitiveness and operational efficiency. Retailers are improving forecasting to reduce the cost of excess inventory. Financial services institutions are detecting fraudulent transactions. Healthcare providers are predicting the risk of disease more quickly. Even modest improvements in the accuracy of predictive machine learning models can translate into billions on the bottom line. The NVIDIA accelerated Data Science Workstation (DWS) solution with RAPIDS enables enterprises and data scientists to tap into GPU-accelerated machine learning (ML) and deep learning (DL) with faster model iteration, better prediction accuracy, and lowest data science total cost of ownership (TCO).

Workstations are the epicenter of creativity and innovation for creative professionals across an array of disciplines, and the introduction of NVIDIA’s Quadro RTX GPUs is fundamentally altering the scope and type of tasks they can perform. Able to render cinematic quality images in real-time, utilize advanced AI to proactively assist in the design or creative process, and power lifelike virtual reality simulations with ease, Quadro RTX Workstations mark the pinnacle of creative potential and professional productivity.

From advanced medical imaging to personal genomics the life sciences research community, healthcare providers and pharmaceutical companies are using NVIDIA GPUs to revolutionize healthcare. GPU-accelerated research tools are shortening the time from insight and discovery to available treatments, delivering advanced medical imaging to guide clinicians, assisting in robotic surgery, even enabling AI (Artificial Intelligence) diagnostic tools based on Deep Learning to provide better research and patient outcomes – while empowering physicians, clinicians, and researchers passionate about improving the lives of others to do their best work.

PNY is proud to sponsor Military & Aerospace Electronics’ Executive Briefing: GPGPU technology ushers-in a new era in embedded computing. The Briefing includes articles on how GPGPU technology is revolutionizing high-performance embedded computing (HPEC) in aerospace and defense applications.

Visitors to I/ITSEC 2018 can meet with our Partner RAVE (Booth #700) to discuss their NVIDIA embedded GPU needs and FoxGuard (Booth #2123) to see the new NVIDIA Quadro RTX 6000 bring stunning realism to an advanced flight simulator.

NVIDIA Quadro GPUs, including NVIDIA Quadro MXM Embedded and GPU Down Solutions, provide the extreme graphics and compute performance demanded by mission-critical aerospace and defense applications. This week at I/ITSEC, the world's largest modeling, simulation, and training conference, which emphasizes themes related to defense and security, attendees are invited to visit PNY Partner’s RAVE and FoxGuard’s booths. Visit RAVE (booth #700) to learn how NVIDIA Quadro GPUs, including Quadro Embedded GPUs are integrated into RAVE’s purpose-built computer hardware solutions optimized for the Modeling, Simulation and Training industry, offer unmatched performance and features. FoxGuard (booth #2123) is demonstrating why and how the NVIDIA Quadro RTX 6000 professional graphics board is ideal for simulation and training applications where rendering is an essential component of the solution, and AI can be utilized to deliver more innovative, cost-effective, and transformational solutions.

As demand for deep learning continues to gain momentum, it’s already changing the way people work. Driving the next wave of advancement in deep learning-infused workflows is the NVIDIA Volta GPU architecture.

In his keynote address at the GPU Technology Conference 2018 in San Jose, NVIDIA founder and CEO Jensen Huang unveiled the new Volta-based Quadro GV100, and described how it transforms the workstation with real-time ray tracing and deep learning.

Bringing unprecedented capabilities in deep learning, rendering and simulation to designers, engineers and scientists, the new products allow professionals to design better products in a completely new way. GPU-accelerated techniques, like generative design, and the ability to conduct complex simulations faster mean businesses can explore more design choices, optimize their designs for performance and cost, and consequently bring ground breaking products to market faster.

Launched at the show, the formidable new AI and compute enabled NVIDIA Quadro GV100, demonstrated how it effortlessly meets the demands of AI-enhanced design and visualization workflows. Delivering 118 TFLOPS of deep learning performance, a 2x GV100 NVLink enhanced workstation showed why it is an ideal choice for AI development.

Tractica Research forecasts that AI software revenue will reach $36.8 billion by 2020. Any work pushing the technology envelope, from analytics or healthcare, to scientific and technical, even aspects of media and entertainment, has big data at its core. More than 19,000 companies use NVIDIA enabled deep learning, and the Quadro GP100 is an ideal development platform compatible with industry standard workstations.